System and method for significant dust detection and enhancement of dust images over land and ocean

ABSTRACT

A new processing capability for dust enhancement over land or water using image data from the Sea-viewing Wide Field of View Sensor (SeaWiFS) has been developed for Naval meteorology/oceanography (MetOc) operations support. The data are captured via direct broadcast high-resolution picture transmission (HRPT) at Navy Regional Centers in Rota, Bahrain, and Yokosuka, and processed at the Naval Research Laboratory in Monterey. The raw data are calibrated, corrected for missing lines and clutter, corrected for molecular scatter contamination, and enhanced through multispectral combination to yield value added products. The processing has been automated completely such that products, generated upon receipt of data, are hosted upon a password protected website typically 60 to 90 minutes from time of initial capture. This invention summarizes the SeaWiFS instrument capabilities, the protocol followed for automated near real-time processing, a physical basis for the NRL enhancements, and specific examples of the products with extension to over-land dust enhancement as enabled by MODIS. It closes with a glimpse of the potential utility of these products from the perspective of the warfighter.

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 10/713,908, filed Jan. 21, 2003 now U.S. Pat. No.7,242,803.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention deals generally with digital enhancement of environmentalsatellite imagery of airborne dust layers over both ocean and landbackgrounds.

2. Description of the Related Prior Art

The detection and tracking of airborne dust from satellite has been oflong-standing interest to both the Navy and academia alike, and there isno shortage of papers treating the subject (e.g., Shenk, W. E., and R.J. Curran, 1974: The detection of dust storms over land and water withsatellite visible and infrared measurements, Mon. Weather Rev., 102,830-837. Carlson, T. N., 1978: Atmospheric turbidity in Saharan dustoutbreaks as determined by analyses of satellite brightness data, Mon.Weather Rev., 107, 322-335. Legrand, M., M. Desbois, and K. Vovor, 1987:Satellite detection of Saharan dust: optimized imaging during nighttime,J. Climate, 1, 256-264. Jankowiak, I., and D. Tanre, 1992: Satelliteclimatology of Saharan dust outbreaks: method and preliminary results,J. Climate, 5, 646-656.) These earlier works make use of various spatialand spectral contrast signatures to identify and retrieve properties ofdust against land and ocean backgrounds.

A recent study (R. Cantu; “The Role of Weather in Major Naval AviationMishaps”, MS Thesis, 106 pp., NPS Monterey) finds that poor visibilityhas factored into approximately 54% of Navy aircraft “Class-A” mishaps,for an estimated annual loss of $51 million. Of these mishaps it wasreported that 56% were preventable had better forecasting and/orobservational tools been available. The current Dust Enhancement Productrepresents a paradigm shift in the way dust is observed from the spaceplatform, and is directly applicable to mitigating this multi-milliondollar liability.

As noted in the reference listing above, several alternative methodsexist for enhancing dust over land and water. Single-channel radiometerimagery with scaling thresholds chosen to enhance a small dynamic rangeof temperature or reflectance values has been applied to visible andinfrared geostationary imagery. The resultant enhancements, usually onlyof marginal visual quality, are highly dependent on the thresholdschosen and hence not suitable for operations where optimal values forthese thresholds may change dynamically over space and time.Combinations of single-channel visible and infrared data is a markedimprovement over the scaling described above, but still sufferscloud/dust ambiguity for lack of multi-channel visible data (i.e., thebasis for the over-water Dust Enhancement Product, which requires blueand short wave infrared channels). The Empirical Orthogonal Function(EOF, or sometimes referred to as “principle component”) method has beenshown to do a good job of enhancing dust over land and water. However,results produced by this method again are highly dependent on thevariability of the scene and hence will provide an inconsistentenhancement depending on background and dimension of the region. Spatialcontrast reduction, which takes advantage of the blurring effect of dustover otherwise sharply-defined terrestrial features, has been appliedsuccessfully for dust detection over land, but often fails over laminarocean backgrounds and is limited over land in regions where the terrainis less variable (e.g., desert plains, where dust incidentally is mostcommon).

None of the previous methods enlist multi-channel visible data to takeadvantage of the inherent spectral variability of dust at thesewavelengths, due primarily to the unavailability of such data at thetimes of those writings. The unified land/ocean Dust Enhancement Productis a novel solution to the daytime dust detection problem. Incomparisons between the visible/infrared combination technique (e.g., asis applied to Meteosat 5 data) and the current algorithm (applied toTerra-MODIS data) for a space and time co-located dust storm event inSouthwest Asia has revealed superior detection of dust by the currentmethod over both land and water. As such, the new technique is thoughtto be of immediate relevance to Navy Meteorology/Oceanography (METOC)operations in several regions of the world that experience significantdust outbreaks (including Southwest Asia, Africa, the Mediterranean, andthe West Pacific).

SUMMARY OF THE INVENTION

The dust enhancement system of the invention is designed to provide anindication of the horizontal distribution of airborne dust layers overboth land and water surfaces for daytime scenes. The regions of dust areenhanced as shades of pink, depending on opacity, against darkernon-dust backgrounds (e.g., green land, dark blue water, and cyanclouds).

The primary advantage of the Dust Enhancement Product is its ability tocall immediate attention to areas where significant concentrations ofairborne dust reside, particularly in regions where even the true colorimagery experiences great difficulty in discerning. The enhancementhighlights mesoscale circulations in optically thin dust over water, andis also useful for identifying volcanic plumes (e.g., Mt. Etna Jul. 26,2001 eruption—image attached). The latter capability is of particularrelevance to both military and civilian aviation. The algorithm isapplicable to a wide range of ocean color instruments in the ocean-onlymode (i.e., true color over land surfaces) or in the ocean/land modeprovided infrared information is available either on the same platformor co-registered with temporally matched imagery from an independentobserving system.

The new dust enhancement technique is in fact a combination of twoindependent enhancement algorithms—one applicable to water backgroundsand the other to land backgrounds. It is based physically on the premisethat silicate aerosols (e.g., mineral dust common to desert sandstorms)preferentially absorb blue light compared to green and red light,whereas cloud particles (liquid or ice) are more or less non-absorbingacross this region and reflect all three components strongly. Therefore,a spectral difference between red and blue light will be much larger fordust than for clouds. In this way, data from a radiometer with spectralresolution capable of measuring independently the red, green, and bluecomponents of reflected solar energy can be combined to further enhanceand decouple the dust signal from clouds. Over water, the spectraldifference is normalized by the magnitude of the reflectance such thatadditional sensitivity to thin (low reflectance) dust is achieved. Theapproach requires correction of the multi-channel data to removescattering contamination from the molecular atmosphere prior tocomputing the enhancement.

Because the land background possesses similar spectral properties to theairborne dust at visible/shortwave-infrared wavelengths, the simpleocean-component described above is insufficient for identifying dustover land. The new technique includes thermal contrast and spectraltransmittance property differences, which require channels in theinfrared part of the spectrum. With its 36 narrowband channels spanningthe optical portion of the electromagnetic spectrum fro 0.4 to 14.4micrometers, MODIS is well equipped to exploit these techniques from asingle platform (although the Dust Enhancement Product may be achievedby combining registered data from a collection of independentplatforms).

The three factors contributing to the over-land component of the DustEnhancement Product are outlined as follows: During the day, cooleremissions from an elevated dust layer contrast against the warmemissions from a heated surface. The same temperature signature holdstrue for clouds, but the same spectral decoupling applied to theover-ocean enhancement applies also to clouds over land. Combining thetemperature and shortwave-differences provides a means to detecting theoptically thick portion of the dust plume. Optically thin regions ofdust will not produce a strong infrared temperature depression. However,spectral differences in thermal (11.0-12.0 micrometers) transmission fordust are very useful in determining areas of thin dust. The signature isopposite in sign to that of thin cirrus. Combining these multispectralfeatures together yields a tractable algorithm for the enhancement ofdust over land. Although the land and water enhancement algorithmsdiffer significantly, the land enhancement has been formulated such thatdust over land is the same hue (shades of pink) as dust over water withminimal discontinuity in saturation across coastal boundaries.

The techniques of the invention demonstrate that the currentenhancement, as applied to the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) radiometer (over water only) and MODIS (water and land), hassignificant potential to benefit the warfighter through its ability toprovide through high spatial resolution satellite imagery a detaileddepiction of atmospheric dust outbreaks with enhancements for opticallythin regimes, as well as other meteorological/land-surface features ofinterest (e.g., clouds, fires, snow cover). Based on these findings, NRLand Fleet Numerical Meteorology and Oceanography Center (FNMOC) havecollaborated to host a subset of these products upon the FNMOC SecureInternet (SIPRNET) beta-webpage for the purpose of i) making this newtechnology available to Navy assets, and ii) receiving feedback usefulfor improving upon its current utility.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1—The SeaWiFS sensor (left) flying aboard the SeaStar platform(right)

FIG. 2—Raw SeaWiFS data from a Navy Regional Center (NRC) receivingstation (left) and the noise/line corrected equivalent (right)

FIG. 3—SeaWiFS High Resolution Picture Transmission (HRPT) true colorimage of the Eastern Atlantic Ocean, Iberian Peninsula, and NorthwesternAfrica, captured by NRC in Rota, Spain (Feb. 13, 2001, 1255Z). A largedust plume wrapping into a baroclinic system is indicated

FIG. 4—SeaWiFS HRPT true color image of the Persian Gulf area, capturedby NRC in Bahrain. Airborne dust is indicated. Phytoplankton isresponsible for the green pigmentation in the littoral regions

FIG. 5—SeaWiFS HRPT true color image of the Korean Peninsula, capturedby NRC in Yokosuka Japan. Yellow arrows indicate areas of biomasssmoke/pollution

FIG. 6—True color (top) and vegetation enhancement (bottom). Bright redindicates regions with abundant plant life. Note seasonal changes inFall vegetation compared against Summer (FIG. 5)

FIG. 7—Coceptual illustration of preferential absorption of blue lightby mineral dust, resulting in yellow reflected light. Clouds scattermore uniformly over the visible wavelengths, resulting in white light

FIG. 8—Examples of thin dust enhancements over the waters of the PersianGulf. Optically thin dust plumes appear as darker tones of red. Landregions in the dust enhancement have been masked

FIG. 9—True color imagery of Typhoon Pabuk making landfall on Japan

FIG. 10—The enhancement captures a volcanic ash plume from the 2001eruption of Mt. Etna. Land regions in the dust enhancement have beenmasked out

FIG. 11—MODIS dust enhancement (0.5 km resolution) over the NorthernArabian Sea, true color over land. Dust over the ocean and appear asshades of pink. Dry lakebeds over land appear as patches of pink.Accompanying text from Navy MetOc post deployment report correspondingto the storm observed in this image

FIG. 12 Enhancement of dust lifted by strong post-frontal winds in theDesert of Southwest North America. Visible channel imagery (top) iscontrasted against dust enhancement (bottom), revealing several pointsources causing downwind plume.

FIG. 13 Dust over Southwest Asia during Operation Iraqi Freedom. Visiblechannel imagery (top) fails to unambiguously resolve significant detailsof dust plumes captured by the enhancement (bottom).

FIG. 14 Demonstration true color (left), original “inclusive logic” dustenhancement (center), and the revised “exclusive logic” dust enhancement(right) for a case study over Nevada.

DETAILED DESCRIPTION OF THE INVENTION

The Satellite Meteorological Applications Section at the Naval ResearchLaboratory (NRL) in Monterey has developed true color, vegetationenhancement, and airborne sand/dust enhancement processing capabilitiesfor the Sea-viewing Wide Field of View Sensor (SeaWiFS) instrument.These products are made in near real time (typically 60 to 90 minutelatency from time of ingest at the receiving stations) using telemetriescaptured at Navy Regional Centers (NRCs) located in Spain (Rota),Bahrain, and Japan (Yokosuka), and populate a password-protected websitefor several predefined sectors within the general coverage regions ofthese centers. Presented here is an overview of the SeaWiFS sensor andits capabilities/limitations, a summary of the procedure, and thecurrent processing architecture. Examples drawn from the archiveddatabase (automated processing of these data commenced Aug. 8, 2001) arepresented to illustrate their quality and potential operational utility.

Platform/Instrument Specifications

The SeaWiFS instrument (Firestone and Hooker, 1996), launched aboard anextended Pegasus rocket on Aug. 1, 1997, is an 8-channel whiskbroomscanning radiometer featuring 8 narrowband (˜20 nanometers (nm) wide)spectral channels centered at 412, 443, 490, 510, 555, 670, 765, and 865nm wavelengths. Carried aboard the SeaStar spacecraft (see FIG. 1),SeaWiFS follows a sun-synchronous (98.3° inclination, 705 kilometer (km)altitude above mean sea level (AMSL)) orbit with a local noon descendingequatorial crossing time. It has a local-area-coverage (LAC) swath widthof 2801 km (corresponding to a maximum scan angle of 58.3°) and anominal spatial resolution of 1.1 km at nadir. Global Area Coverage(GAC) is also available at 4 km degraded resolution and a reduced 1502km swath (data limited to 45° scan angle). The 10-bit digitized data aretransmitted down to Earth at an L-band frequency of 1.70256 Gigahertz(GHz). The instrument scans across 1285 picture elements at a rate of 6Hz. Typical High Resolution Picture Transmission (HRPT) stationtelemetries include typically 4000 to 5000 scan lines of data (dependingon local topographical/anthropogenic obstructions), translating to 11-13minutes of capture time from the nominal initial-capture time stamp onthe filename.

The primary function of SeaWiFS is to detect and monitor ocean color.Specifically, it has been designed to retrieve the small component (˜5%of total signal) of water leaving reflected radiance whose spectralsignature provides useful information about ocean phytoplankton content(expressed in terms of chlorophyll-A retrievals). A varying along-tracksensor tilt angle of +/−19.8° is used to avoid regions of sun glint(specular reflection of the solar disk upon the ocean surface) wherechlorophyll-A retrievals cannot be performed. The information obtainedfrom such retrievals is valuable to marine research and industryinterests. Applications include guidance to the fishing industry, divervisibility information, surface currents, physical oceanography, generalmarine biology, and climate impacts on the marine ecosystem. Whileatmospheric constituents such as cloud, dust, and aerosol are consideredas noise to ocean color algorithms, the current research regards thesecomponents as the signal of interest. This description of the inventionreport details how SeaWiFS data are applied to enhance atmospheric andsurface features/phenomena.

Instrument Advantages

The main advantage provided by SeaWiFS over optical spectrum instrumentsflown on the geostationary (e.g., the Geostationary OperationalEnvironmental Satellite (GOES) constellation, Meteosat 5,7,Geostationary Meteorological Satellite (GMS), and the Indian NationalSatellite System (INSAT)) and other polar orbiter (e.g., NationalOceanographic and Atmospheric Administration (NOAA), DefenseMeteorological Satellite Program (DMSP), and many others) platforms forthe current application is its multiple narrowband visible channels thatprovide a true color imaging capability.

Instrument Limitations and Other Caveats

The main deficiencies of SeaWiFS in terms of 24-hour operational utilityare its lack of infrared channels, its limited temporal resolution, andproprietary nature. Infrared data are useful for nighttime imaging,cloud particle size estimation, and airborne dust enhancement over landsurfaces, among other uses. At mid to low latitudes, only one to threepasses are available near local noon—providing at best a “snapshot” ofthe synoptic-scale situation on any given day. Captures are increasinglyfrequent at the higher latitudes owing to the orbital geometry. Typicalcaptures for the regional centers currently processed are on the orderof 2 to 3 per day. Thus, SeaWiFS products are most useful whenjuxtaposed with geostationary data that offer the temporal informationrequired to infer details of the atmospheric circulation.

Because SeaWiFS was built, launched, and operated by Orbital SciencesCorporation (OSC), the data are formally available to the general publicon a research-only basis and are provided at cost. The NationalAeronautics and Space Administration (NASA) has contracted with OSC forthe research use of a 5-year data set commencing in September 1997.Researchers who have registered as authorized SeaWiFS user (registrationat http://seawifs.gsfc.nasa.gov/SEAWIFS.html) are granted permission toobtain the data from the NASA SeaWiFS archive, approved NASA Space ActAgreement ground station, or authorized individual. As authorizedreceiving stations, the NRCs providing near-real-time LAC data for thisresearch are covered under the existing agreement, with the onlycriterion being that NRL does not disseminate the LAC-derived productsto the general public in near real-time.

Data Processing

Several options exist for processing the SeaWiFS telemetry, depending onthe format of the data. The formats currently received at NRL are NASALevel-0 (L0; 10-bit Data Capture Facility frame-formatter files), withTerascan Data Format (TDF) (pre-calibrated and navigated) upon specialrequest. Data from the NRCs are in NASA L0 format, and require severalpreprocessing steps to calibrate and navigate the data as describedbelow. For reasons described further along, the current approach tocreating SeaWiFS products is based on NASA L0 data and is independent ofthe Terascan software package. Several approaches have been iteratedupon in arriving at the current procedure including various blendedcombinations of software packages—some more appropriate for rapiddeployment to remote operational centers and others geared towardin-house processing with all the conveniences of locally available thirdparty software. While the purpose of this description is to summarizethe current approach, alternative options and their associateddata/software requirements will be touched upon in passing.

Worth noting is that the Perl programming language, running on the Linuxplatform, was selected as the scripting language for automatedprocessing of SeaWiFS data. Perl draws from the strengths of manypopular scientific programming languages such as C++ and Fortran whileenhancing on the capabilities of other standard shell scriptinglanguages. The Perl scripts written in support of the current SeaWiFSprocessing perform the requisite secretarial operations of dataretrieval, file and directory manipulation, execution and statusmonitoring of telemetry updating and data processing codes,dissemination of imagery products to the website, and generalhousekeeping. Perl remains the lone constant among all processingoptions discussed in this report.

SeaDAS Processing Option

One of the available SeaWiFS processing options is the GSFC SeaWiFS DataAnalysis System (SeaDAS) software package (Fu, G., K. S. Baith, and C.R. McClain, 1998: SeaDAS: the SeaWiFS data analysis system, Proc. of4^(th) Pacific Ocean Remote Sensing Conf, Qingdao, China, Jul. 28-31,1998, 73-79), which was designed specifically for viewing and basicmanipulation of SeaWiFS data. It uses an embedded runtime InteractiveData Language (IDL) license as its operating paradigm. The SeaDASsoftware is designed to operate on NASA L0, Level-1A (L1A; rawspacecraft and instrument telemetry retained with convertedgeo-location, instrument telemetry, and selected spacecraft telemetry),and Level-1B (L1B; sensor calibration applied to the data). L1A and L1Bdata are stored in Hierarchical Data Format (HDF). The software includesa graphical user interface enabling straightforward viewing andmanipulation of the data, as well as command-line functions useful forincorporating into scripts as batch jobs. The SeaDAS package was usedearly on in this research to examine the general utility of SeaWiFS datain terms of coverage and data quality before proceeding with customizedapplication. While SeaDAS is not used in the current processing (in theinterest of gaining a higher level of control over product displayoptions), several functions were adopted from this package forconversion from NASA L0 to L1B HDF files and data correction. SeaDASremains an extremely useful stand-alone tool for SeaWiFS datavisualization, and is available via the Internet athttp://seadas.gsfc.nasa.gov. The website includes versions of SeaDAS forseveral operating platforms and has a limited level of helpful onlinesupport.

The Currently Implemented Processing Scheme

A simple overview of the current processing flow is as follows: I)SeaWiFS LAC telemetry captured at an NRC site and converted immediatelyto NASA L0 formatted file, II) Data sent to the satellite data storagefacility at Naval Oceanographic Center (NAVO) at Stennis Space Center inMississippi via file transfer protocol (ftp) push, III) Data are pushedvia ftp to NRL Monterey from NAVO, IV) A periodic local cron jobsearches for receipt of new files, and if found: V) SeaWiFS processingscript commences to calibrate, clean, apply atmospheric corrections, andgenerate products, VI) Products are uploaded to a password protectedNIPRNET website. Latency of near real-time products is generally on theorder of 1.5 hours from time of ingest at the NRC site. The remainder ofthis section provides additional details on the steps outlined above.

NRC Stations

The mid-latitude NRCs included in the current NRL processing are Rota[36.63N, 6.34E], Bahrain: [26.16N, 50.39E], and Yokosuka [35.28N,139.67E]. Each station captures SeaWiFS passes on or around its localnoontime hours (1100-1400 Z for Rota, 0700-1000 Z for Bahrain, and0130-0430 Z for Yokosuka). NRL receives the NASA L0 data from NAVO asGnu-compressed files typically 1 hour after the nominal capture time.File sizes vary according to pass coverage, with typical values rangingfrom 20 to 80 Megabytes. The 55 Gigabyte hard drive currently used as astorage bay for raw data, algorithms, and products is sufficient forseveral months of online storage. All data are eventually archived toCDROM or tape storage in a hierarchical structure partitioned bystation, year, month and day.

Data Calibration

A cron job launched every half hour initiates the processing of newlyreceived SeaWiFS data. A loop over all files present in the ftp “receivedirectory” checks for any new and unprocessed data. If a new file hasarrived, it is uncompressed and copied to the local storage drive forsubsequent processing to L1B calibrated radiances (expressed inmW/cm²-sr-μm). Binary executables taken from the SeaDAS software packageare used for this purpose, and require an updated SeaStar ephemeris forcorrect earth location (SeaWiFS requirement of 1-pixel geolocationaccuracy). These data are available at ftp.samoa.gsfc.nasa.gov, and areupdated on the local system if the current elements file is found to bemore than one day old. Any errors incurred during the uncompressing orcalibration steps are reported to a log file and the script is exitedgracefully.

Sector Definition and Noise Corrections

Upon completion of L1B processing, an IDL script for customization ofthe data is spawned. Latitude, longitude, sun/sensor geometry, andSeaWiFS channel variables are extracted from the HDF data. A table ofpre-defined sectors for each station is included and readily updated fornew operational areas of interest or one-time case studies. Definitionof these sectors is based on the specification of latitude/longitudeboxes (lower left and upper right corners). Owing to the scanninggeometry, regions near the edge of the swath suffer spatial resolutiondegradation. As a result, imagery for these pre-defined sectors mayinclude only partial coverage at compromised quality. A minimum datacheck of 20% coverage over the sector in question is enforced prior toproceeding with the processing. To ensure that the optimal informationcontent for every SeaWiFS pass is provided, a “floater” sector isincluded which selects a box data away from the swath edges (poorresolution) and the horizons (avoiding numerous scan line drop-outs).The trade off for high image quality is a random coverage area from passto pass.

Once the sector of interest has been defined (or loops over numeroussectors), an embedded loop over SeaWiFS channels is performed forcorrections of pixel noise and line dropouts. Noisy pixels appear in theimagery as a “salt and pepper effect” owing to the presence of spuriousvery high or very low data values. Line dropouts appear in imagery asblack scan lines devoid of data or filled with noise. Theseimperfections are present in the data due to a combination of possiblehardware limitations/problems and clutter associated with nearbystructures. Cleaning of the data is accomplished in two ways. Linesidentified as dropouts are replaced by previous good scan lines (badlines at the very beginning or end of a data stream are replaced bytheir nearest good neighbor). If multiple adjacent dropouts are present,the previously patched line is used once again. This correction losesits cosmetic effectiveness for large data gaps, but provides markedimprovements in cases of spurious line dropouts. Noisy pixels, assumedto be spurious, are identified using a threshold based on the absolutevalues of pixel derivatives computed along a scan line. When flagged,they are replaced by an average of the two adjacent pixels on the samescan line. Noise pixels on the edges of lines are simply replaced by thevalue of their nearest neighbor. FIG. 2 illustrates the result ofcorrections applied to a subsector of SeaWiFS data collected over theAlps. The overall effect of these bad line and pixel noise correctionsis dramatic, and regarded as an essential step in the production ofhigh-quality SeaWiFS imagery. Additional noise corrections (not yetimplemented) may be required to correct pixels where one of the channelsis missing data (but was not flagged by the noise threshold). Thisresults in a magenta, yellow, or cyan-biased pixel values in the RGBproducts.

Atmospheric Corrections

The effects of atmospheric molecular (sometimes referred to as“Rayleigh”) scattering must be removed from the data before creation oftrue color products. The amount of Rayleigh scatter is proportional toλ⁻⁴ (wavelength) and hence is strongest for the shorter (e.g., blue,violet) wavelengths. If left uncorrected it will manifest in the imageryas a semi-transparent milky blue haze increasing in opacity toward theedges of the swath (as higher sensor angles peer through an opticallythicker atmospheric path), detracting from image contrast and clarity.To remove this undesirable component of the satellite signal, we use aradiative transfer model (Miller, S. D., G. L. Stephens, C. K. Drummond,A. K. Heidinger, and P. T. Partain, 2000: A multisensor diagnostic cloudproperty retrieval scheme, J. Geophys. Res., 105, No. D15, 1995-1997) tosimulate the clear sky Rayleigh scatter over a dark surface as afunction of the solar zenith, satellite zenith, and solar-satelliterelative azimuth angles. Pre-calculated look-up tables were created foreach SeaWiFS channel and the correction was applied on a pixel-by-pixelbasis, provided the sun/satellite geometry in the Level 1B data. Theprimary caveat associated with this correction is the assumption of acloud-free Rayleigh contribution that in some instances may result in anover-correction of pixels containing high clouds near the edges of theswath. Visual inspection of imagery has not indicated a significantdistortion owing to this effect, however. Regions near the edge of theSeaWiFS swath should in general be interpreted with cautious regard tothe possible distortions owing to larger atmospheric path lengths,three-dimensional effects (parallax), and possible bow-tie effects.

Scaling

Once the data have been calibrated, cleaned of noise, and corrected forRayleigh scatter, the spectral radiances (I_(λ)) are converted toequivalent isotropic reflectances (R_(λ)) according to the relationship:

$\begin{matrix}{{R_{\lambda} = \frac{\pi\; I_{\lambda}}{\mu_{o}F_{o,\lambda}}},} & (1)\end{matrix}$where μ_(o) is the cosine of the solar zenith angle and F_(o) is thesolar spectral flux at wavelength λ. By definition, the isotropicreflectance represents the reflectance produced by an equivalentlambertian surface, and varies from 0.0 to 1.0. Any spurious valuesgreater than 1.0 or less than 0.0 are thresholded by these upper andlower bounds. The reflectances are scaled by log₁₀ in an attempt toequalize the imagery and prevent the very bright cloud/snow pixels fromdominating the information content of the products. These scaledreflectances are byte-scaled over the 256-element color table range ofthe IDL palette, with a minimum logarithmic reflectance of −1.65 mappedto the minimum saturation value (0) and 0.0 mapped to the maximum (255).This procedure is detailed in the computer program code listing in theAppendix.

The scaled data are warped to a mercator projection corresponding to thesector of interest. This procedure is currently the most computationallytime-consuming component of the processing, and options for optimizationor alternative approaches are currently being explored.

The final image products are created as jpegs at 80% of full quality.File sizes range from 200 to 800 Kilobytes depending on the sector andimage information content. The true color, vegetation enhancement, anddust enhancement products for various stations and subsectors are copiedto a password-protected online holding directory on the NRL SatelliteMeteorology web page (http://kauai.nrlmry.navy.mil/sat_products.html).

True Color Enhancement

To produce the true color images, the blue (412 nm), green (555 nm), andred (670 nm) SeaWiFS data are loaded into the respective blue, green,and red IDL “color guns.” The data comprise a single matrix of dimension(n,m,3) where n=along-scan direction and m=along-track direction. Theresultant imagery is similar, but not identical, to what would beobserved by the human eye from the vantage point of outer space; cloudsas shades of gray to white, ocean surfaces as shades of blue, and landfeatures as earth-tones and green in regions of heavier vegetation.

Examples of true color SeaWiFS imagery are shown for the Rota, Bahrain,and Yokosuka regional centers in FIGS. 3-5, respectively. FIG. 3demonstrates the immediate ease with which significant airborne dust isidentified over oceans. The image depicts a widespread easterly plume ofSaharan dust captured within the synoptic flow of a baroclinic systemoff the coast of Spain and Portugal. SeaWiFS is useful in monitoringthese dust plumes as they migrate across the Atlantic Ocean (oftenreaching the eastern United States). The green colors in littoralregions of the Persian Gulf in FIG. 4 correspond to a higher amount ofbioactivity (e.g., plankton) in nutrient-rich waters. Cloud streets overland and evidence of biomass burning in the form of diffuse smoke plumesare apparent over Southeastern Asia in FIG. 5. The NRC in Yokosuka alsocaptures seasonal dust storms blowing off the Gobi desert. Owing to thegeneral circulation of the atmosphere, large Gobi and Saharan duststorms may reach the continental United States (the former crossing thePacific with the “Roaring Fourties”, and the latter crossing theAtlantic with the Tropical Trades.

Vegetation Enhancement

Because green vegetation possesses a relatively high albedo compared tosoil in the “reflective” or shortwave infrared (0.7-1.3 μm), a combinedvis/shortwave-IR product that enhances vegetation regions is generatedin a way similar to the true color enhancement described above. Thisvegetation enhancement product is similar to what has been produced formany years for land-usage studies with Landsat TM data. The 670 nm (red)channel in the previously discussed RGB true color enhancement isreplaced by the 865 nm (SeaWiFS channel 8) channel data. This results ingreen vegetation appearing as bright red and appears usually in sharpcontrast to surrounding regions devoid of vegetation. An example of thisproduct is shown in FIG. 6. The vegetation enhancement may prove usefulin identifying potential fetch areas for airborne dust.

Airborne Dust Enhancement

An important capability of SeaWiFS, resulting from its multiplenarrowband visible channels and indicated in the examples above, isdiscrimination between airborne dust and clouds/smoke via true colormethods. Fine-grain particles lifted from sparsely vegetated and drydesert surfaces by strong frictional winds (typically greater than 10m/s, usually by meso- and synoptic-scale circulation features) give riseto significant plumes of atmospheric dust. These plumes follow the lowto mid-level atmospheric circulation and may also become incorporatedwithin synoptic scale baroclinic systems (giving rise on occasion tounusually “mud rain” storms that have been reported to deposit a coatingupon everything in their path).

Perhaps more importantly to Navy interests, airborne dust hassignificant detrimental impacts on slant-range visibilities (withvisible optical depths that can exceed 3.0 and accompanied byvisibilities less than 1 nautical mile) and also poses a serious hazardin terms of its potential to damage turbine engines. This is ofparticular relevance to aircraft carrier operations in regions of theworld prone to widespread dust outbreaks. Because airborne dust retainsthe earth tone hues (usually light brown or yellow, depending on thesource region) of the underlying surface, it is readily identifiable inmost cases from a true color image, especially for optically thickerdust plumes. For regions of less dust (where its effects on slant rangevisibility may still be appreciable), an additional enhancement beyondtrue color is required. NRL Monterey has developed a dust enhancementalgorithm for SeaWiFS that attempts to provide this capability.Enhancement of dust is predicated on basic spectral properties thatcharacterize its appearance at visible wavelengths. As indicated in FIG.7, yellow light (characteristic of many desert dust plumes) results fromthe removal of blue light (via preferential absorption by the dust).This spectral contrast is even more dramatic between the blue andshortwave infrared channels. This preferential absorption does not occurto the same magnitude in liquid/ice clouds (explaining why clouds appearas shades of gray to white, indicating nearly equal reflectedintensities of red/green/blue light, in a true color composite). Bydefining a dust enhancement parameter “Δ”:

${\Delta = \frac{\alpha_{865} - \alpha_{412}}{\alpha_{865} + \alpha_{412}}},$where α_(λ) are reflectances at wavelength λ (nm), any dust component ofthe image will be enhanced. Cloud pixel values, which are relativelyspectrally flat, will be diminished in brightness owing to the smallnumerator difference. The logarithm of the Δ parameter is scaled between−0.45 and 0.20 and loaded into the red channel of the RGB composite.

Owing to the significantly reduced red component for cloud pixels in thedust enhancement (small values of the Δ parameter) image, cloudstypically appear as shades of light blue or cyan. Retaining the originalgray/white cloud hues in the dust enhancement (for cosmetic purposesonly) requires the implementation of a simple cloud mask—pixels flaggedas cloudy can be re-assigned to their original red channel values.Clouds in the imagery are identified first by computing the mean scaledmagnitude and standard deviation of the 412, 555, and 670 nm channels.Mean magnitudes exceeding 50% and standard deviation less than 2.5% areused as thresholds for positive cloud identification. The simple premisebehind choosing these thresholds is that clouds are in general bright(high brightness magnitude) and relatively spectrally flat over thevisible (low standard deviation). Pixels flagged as “cloudy” are resetto the red channel value in the Δ parameter. As the dust enhancementenhances land surfaces also, a land mask is applied to this product.

FIG. 8 demonstrates the dust enhancement product covering the PersianGulf region. The top panel pair reveals that while the true colorimagery is useful in identifying the significant dust plumes, the dustenhancement reveals a far more extensive coverage of airborne dustcirculating within the Gulf. The center and lower panel pairs furtherillustrate this enhancement capability. While not necessarily posing thesame hazard to aircraft engines as the optically thick (bright) plumes,these areas of enhanced aerosol have detrimental impacts on slant-rangevisibility. Although more difficult to detect than over the oceans, thepresence of significant dust over bright and/or sparsely vegetated landsurfaces can in some cases be inferred by noting contrast reduction inthe background terrain. The lack of an infrared channel on the SeaWiFSinstrument precludes the technique of detecting elevated (cooler) dustabove a warm surface background via emission measurements. The utilityof EOS-MODIS, which provides 36 channels (including narrowband red,green blue, NIR and LWIR bands), will be explored in this capacity.

Other Utilities

The global coverage afforded by the polar orbiting platform providesSeaWiFS with opportunities to survey many atmospheric and terrestrialphenomena. Its utility as an additional sensor for tropical stormmonitoring (e.g., Typhoon Pabuk shown in FIG. 9) may increase inrelevance in light of the current mechanical problems aboard theGeostationary Meteorological Satellite (currently providing coverage ofthe Tropical Western Pacific and the primary satellite observationplatform supporting the Joint Typhoon Warning Center). Unexpected eventsprovide unique opportunities to examine additional capabilities of theSeaWiFS channels. The eruption of Mt. Etna, shown in FIG. 10,demonstrates an additional utility of the dust enhancementproduct—revealing features of the ash plume difficult or impossible todetect in panchromatic or even true color renditions. Depending on theircomposition and concentration (not all volcanic plumes are comprised ofmaterials that will be enhanced by the currently detailed dustenhancement), such plumes may be tracked around the globe, providinginvaluable guidance to the commercial airline industry for avoidingthese potentially serious flight hazards. The detection capabilities ofSeaWiFS offer a valuable asset to Volcanic Ash Advisory Centers (VAACs;e.g., http://www.ssd.noaa.gov/VAAC/washington.html) distributedworld-wide whose charter is to monitor and track these plumes globally,and also the Geneva-based World Meteorological Organization (WMO; e.g.,http://www.wmo.ch/indexflash.html), which coordinates global scientificactivity among an international panel of participants.

New Processing Formats

The SeaWiFS processing as discussed in this description is well suitedfor the creation and analysis of high quality SeaWiFS imagery productsin an automated, near real-time framework.

For the reasons cited above, efforts to reproduce this processing in afashion entirely independent of third party software have resulted in ananalogous package that assumes receipt of SeaWiFS data in TeraScan DataFormat and proceeds to customize the data in the FORTRAN-90 programminglanguage. True color and dust enhancement image rendering is thencompleted using the “rgbimage” TeraScan intrinsic function. ThisFORTRAN-based version is actually more efficient and hence runs fasterthan its IDL counterpart.

Blended Products

SeaWiFS is only one of several telemetries available for processing atNRL Monterey. It is a straightforward procedure to incorporate data fromthese other sensors within the same image to produce a multi-sensorproduct. For example, the masked-out land areas in the dust enhancementproduct may be filled in by temporally matched geostationary data (e.g.,Meteosat 5 over Bahrain, MeteoSat 7 over Rota, and GMS over Yokosuka).Infrared information can be used to enhance regions of elevated dustover the land in certain situations. As NRL also has at its disposalanalysis and forecast data from its mesoscale (Coupled Ocean-AtmosphereMesoscale Prediction System)™ and global scale (Navy Operation GlobalAtmospheric Prediction System) models, it may also prove useful to blendthese data into the products. Using again the example of the dustenhancement product, inclusion of the COAMPS™ 925 mb wind field in theform of vectors overlaid upon the imagery may serve not only as aqualitative validation of the model analysis but also provide the METOCofficer in the field with a means to predicting the migratory behaviorof significant dust plumes in the short term. Overlay of short-termforecasts of the frictional wind over land (winds near the surface thatare responsible for lifting the dust into the atmosphere) may also be ofuse. Of course, any such multi-sensor and model-fusion products willrequire a training module identifying the various components, strengths,and weaknesses.

These data are of immediate relevance to both the initialization andvalidation of the Navy Aerosol Analysis and Prediction System (NAAPS; aglobal, multi-component analysis and modeling capability to produceaerosol products from satellite data and surface-based measurements).The goal of NAAPS is to improve the forecasting/nowcasting of visibilityrestrictions caused by aerosols, including dust, smoke, and volcanicash. Information and forecast products associated with NAAPS may befound at http://www.nrlmry.navy.mil/aerosol.

Extension to MODIS (Moderate Resolution Imaging SpectroradiometerProcessing)

The MODIS instrument offers a similar true color imaging capabilitytogether with improved spatial resolution (up to a factor of 4 overSeaWiFS HRPT). FIG. 11 provides an example of the MODIS dust enhancementover the Northern Arabian Sea. Plumes of dust over ocean and drylakebeds on the desert interior (often serving as sources for dust) areenhanced as shades of pink. Of specific relevance to the dustenhancement product is the availability of thermal infrared channelsthat enable the discrimination of elevated (cooler) dust plumes overwarm backgrounds. See the Appendix for the computer program code listingimplementing this technique. On-board calibration of MODIS radiancessupports accurate physical retrievals of aerosol optical properties. Atechnique for producing 250 m true color imagery using 500 m blue/greenand 250 m red channels has also been developed. This high spatialresolution capability is enhanced by temporal coverage when consideringthe Terra (MODIS: 1030 descending node), SeaStar (SeaWiFS: 1200descending node), and Aqua (MODIS: 1330 ascending node; platform to belaunched in April 2002) platforms in concert. This quasi-geostationary3-hour loop will provide valuable information on cloud and/or dustmotion. An enabling document for the MODIS land/ocean significant dustenhancement will be published in the Geophysical Research Letters.

Over-Land Algorithm Applicable to MODIS

Over bright desert backgrounds, dust is exceedingly more difficult todetect than over water. Here, additional information from the infraredis required to separate the dust signal from the other spectrallysimilar components of the scene. Table 1 lists the MODIS channels usedin the current dust enhancement. Radiative transfer model simulations ofRayleigh (molecular) scatter as a function of wavelength andsolar/sensor geometry were used to create look-up tables for removal ofthese limb-brightening contributions from channels 1-4 as noted.

TABLE 1 MODIS Channels Used in Dust Enhancement Channel λ(mm)^(a)Resolution(km) Description   1^(b) 0.645 0.25 Red   2^(b) 0.853 0.25Reflective IR   3^(b) 0.469 0.50 Blue   4^(b) 0.555 0.50 Green 26 1.381.0 Shortwave Vapor 31 11.0 1.0 IR Window 1 32 12.0 1.0 IR Window 2^(a)Central wavelength for channel. ^(b)Rayleigh scatter removed.

The premise for the over-land enhancement is threefold: i) elevated dustproduces a depressed brightness temperature against the hot skintemperature of the land background, ii) this cool layer of dust can bedifferentiated from water clouds having the same radiometric temperaturebased on its coloration properties, and iii) mineral dust often producesa positive (and of opposite sign to cirrus) 12-11 μm difference [e.g.,Ackerman, 1997].

In formulating this component, the blue and green color guns remain thesame as in the over-water algorithm, and the red color gun combines theprinciples of (i)-(iii) above in the following way:D _(lnd) =L1+L3−L4+(1.0−L2)  (3)scaled over [1.3, 2.7], where non-dimensional terms L1-L4 and associatedscalings are specified in Table 2, and

$\begin{matrix}{T_{dyn} = \left\{ \begin{matrix}{{T_{\max}(31)} - 21} & {{if}\mspace{14mu}\left( {{T_{\max}(31)} < {301\mspace{14mu} K}} \right)} \\{{\left( {{T_{\max}(31)} - 273} \right)/4} + 273} & {{otherwise}.}\end{matrix} \right.} & (4)\end{matrix}$All brightness temperatures (T) are given in kelvins and T_(max) is themaximum pixel temperature in the current scene. Typical thermal scalingranges over Southwest Asia during the summer months are [285, 325 K],but may vary significantly over time, location, and synoptic weathersituation. Reflectivities (R) listed in Table 2 are normalized.

TABLE 2 Terms for Over-Land Dust Enhancement Term ExpressionNormalization Bounds L1 T(32) − T(31) −2 → 2 K L2 T(31) T_(dyn)(31)^(a)→ T_(max)(31) L3 2R(1) − R(3) − R(4) − L2 −1.5 → 0.25 L4 R(26) > 0.05 ?0, else 1 (n/a) ^(a)As supplied in equation 4.

Normalization bounds were determined experimentally based on a widevariety of dust case studies, with values selected toward optimizingdust contrast while maintaining an enhancement appearance consistentwith the over-water algorithm. The dynamic temperature scaling (Equation4) was introduced to reduce seasonal and diurnal effects giving rise tofalse detection over cold land. Thin cirrus in this formulation producescompeting effects—the depressed 12-11 μm difference (L1 term in 3, e.g.,Inoue [1985]) is offset by positive contributions from coloration viapartial transmittance of land reflectance and sufficiently depressedbrightness temperatures (L2 and L3 terms, respectively) to produce falsedust enhancements. The 1.38 μm reflectance, situated in a water vaporabsorption band, was included as a filter (L4) for cirrus (which resideat levels above most of the tropospheric vapor; see Gao et al. [1998]).

4. Two Examples

Comparisons against standard visible (0.65 μm) imagery scaled to thefull dynamic range of the image are presented to illustrate the improvedability of the technique to detect dust. FIG. 12 depicts a heavy dustplume advancing through western Texas in the wake of a springtimefrontal passage. Strong westerlies at the surface loft dust frommultiple point-sources (revealed in the enhancement as sharply-definedplumes), and the deepening diffuse plume (characteristic of dust insuspension) curls downwind toward the northeast due to backing windswith height associated with post-frontal cold air advection over theregion. While the visible imagery gives some indication of the diffuseplume via contrast reduction, the dust signatures are by no meansunambiguous and represent only a small subset of the extent of dust inthe scene as revealed by the enhancement.

In a second example (FIG. 13), collected during the active militaryphase of OIF, waves of post-frontal dust surge through the SouthwestAsia domain. This event was part of a series of major dust impulsesassociated with the passage of a strong baroclinic system, resulting ina substantial downtime with reduced air support for infantry on theground in southern Iraq. Improvements to detail and distribution ofsignificant dust areas in comparison to the visible imagery are readilyapparent. The dust front crossing Kuwait and entering the NorthernArabian Gulf maintains a similar appearance across the land/seaalgorithmic boundary. A field of cumulus clouds on the Iraqi/Saudiborder and a band of cirrus over southern Qatar both appear cyan. Thelighter cyan of cirrus west of Qatar arises from the over-wateralgorithm not incorporating the MODIS 1.38 μm channel screen.

Equation 3 follows an “inclusive” approach whereby all scaled andnormalized dust-detection components (L1,L2,L3, and L4) have equalopportunity in contributing to the final D_lnd red-gun enhancement term.Whereas this logic aggressively seeks out potential areas of dust in thescene, the cost of this aggression is a high frequency of false alarms(where non-dust regions are enhanced as possible) dust. The most commonfalse alarms involve cold land, cloud shadows, thin cirrus, and sunglintupon unmasked lake bodies. In these cases, one or more of the termscomprising D_lnd are significant. An amendment invoking a new“exclusive” logic scheme has recently been developed, whereby D_lnd isredefined according to:D _(—) lnd _(—) new=L1*(1.0−L2)*L3*(1.0−L4), scaled over [0.35,0.75]  (5)where the L terms are identical to those defined in Table 2. In therevised logic above, all four components must simultaneously besatisfied for the land pixel in question in order for it to beclassified as “dust.” The exclusive logic method is by construct farmore conservative in what it classifies as dust. The downside to thisconservatism is that not all dust regimes produce strong signals in allfour L terms (for example, the Split-Window L2 terms decreases instrength for very thick dust), and the compensating effects of otherstrong terms are limited since now it only takes a single zero-valuedterm to set the entire D_lnd_new term to zero. The upside to the methodis that the false-alarm contributions in the scene are reducedsignificantly, and regions classified as “dust” have much higherconfidence levels. The exclusive definition also allows for moreaggressive scaling bounds to D_lnd_new, mitigating in part the problemsmentioned above. Performance testing for the new method is ongoing, withonly minor corrections to scaling bounds anticipated. The new techniquealso eliminates the need for topography corrections in handling coldland temperatures . . . which manifested in a “blocky” appearance ifapplied to the previous scheme.

FIG. 14 illustrates the impact of the new logic as applied to a dustfront in Nevada. In the left panel, a true color image reveals thepresence of the optically thicker portions of the plume. The dustenhancement following Equation 3 is shown in the center. Here we notethat surrounding non-dust regions, particularly in the left portion ofthe image corresponding to the Sierra Nevada mountain range, assume areddish appearance owing to cold land contributions. The far-right panelof FIG. 14 demonstrates the result of applying the revised logic(Equation 5) to the same data. Non-dust regions are shown to be lessambiguous, and a stronger contrast between the plume and itssurroundings is realized. In spite of the superior performance for thiscase study, there are other cases where small L1, L2, L3, or L4 terms ofD_lnd lead to local suppression of the dust signal, and an inferiorenhancement compared to the original (Equation 3) method. As such,Equation 5 should not supplant Equation 3, but rather be considered as avariation of the technique providing superior results in certain dustscenarios.

CONCLUSION

A new method for identifying dust over water and land backgrounds hasbeen developed for applications to multispectral satellite imagery.Previous methods are combined with new spectral capabilities availablefrom the MODIS sensors aboard EOS Terra and Aqua to enhance dust asshades of pink while clouds/land appear cyan/green. The method iscomposed of two algorithms (over land and water) tuned to maintain asimilar enhancement of dust crossing coastlines. With minor refinementsto digitally mask those areas of enhanced dust (based on thresholdswithin the three-dimensional color space), the technique would appear tobe well-suited as a starting point for overland dust property analyses.

A new processing capability for generation of true color, vegetationenhancement, and over-water dust enhancement imagery from the 8-channelSeaWiFS instrument has also been developed. Near real-time processing oftelemetry captured from Navy Regional Centers in Rota, Bahrain, andYokosuka currently populate the following password-protected website onthe NRL Monterey satellite page at 1-2 hourlatency:http://kauai.nrlmry.navy.mil/archdat/swf_nrc/

Two main points become clear from the above commentary: 1) SeaWiFS andother sensors offering true color imaging capability are of significantvalue and in high demand for Navy operations, and 2) in spite of thefact that such data has been available for some time now from thesatellite platform (e.g., LandSat-™) this capability has not beentransitioned effectively to the end-users who require it (i.e., thosedeployed in the field). In turn, the twofold intent of this descriptionhas been to demonstrate the current in-house processing capabilities forthe SeaWiFS sensor and articulate this operational demand. A logicalextension to these statements is that there exists a pressing need forX-band receiving stations in perennially sensitive regions of the worldfor the purpose of capturing MODIS and other high-data-rate (both multi-and hyper-spectral) satellite telemetries. The potential savings interms of both loss of life and equipment (R. Cantu; “The Role of Weatherin Major Naval Aviation Mishaps”, MS Thesis, 106 pp., NPS Monterey)which is reported at a 51 million dollars annual loss in Navy equipmentattributed to visibility-related meteorological phenomena such asaerosols, dust, and atmospheric trapping layers) owing to improved METOCguidance gained from these state-of-the-art sensors far outweighs theone-time cost of the receiving stations themselves.

Although this invention has been described in relation to an exemplaryembodiment thereof, it will be understood by those skilled in the artthat still other variations and modifications can be affected in thepreferred embodiment without detracting from the scope and spirit of theinvention as described in the claims.

1. A system for detecting and enhancing meteorological imagery of dustclouds over land and water, said system comprising: a collector forstoring multispectral optical-spectrum imagery having multiple channelsrelating to different wavelengths across the visible, shortwaveinfrared, and thermal infrared portions of the optical spectrum; and aprocessor coupled to said collector, said processor operable to receivesthe multispectral optical-spectrum imagery and processes the digitaldata by, performing a numerical atmosnheric correction for removal ofmolecular scatter within all of the visible-spectrum channels, based onradiative transfer calculations stored in pre-computed look-up tablesand indexed as a function of solar and sensor geometry, determining thepixel background for each pixel of the multispectral optical-spectrumimage by combining known earth location with a terrestrial database,employing a background-dependent algorithm to compute the dustenhancement parameter Δ, displaying the multispectral optical-spectrumimagery coupled to the processor, said displaying the multispectraloptical-spectrum imagery comprises a red, blue and green color fordisplaying the visible light spectrum via a hue/saturation decomposedcolor technique, creating a false color composite, wherein said falsecolor composite is created by loading the background-dependent Dvariable into a red color gun, loading log-scaled percent-reflectanceinto blue and green color guns of said false color composite, and theresultant imagery rendered depicts atmospheric dust as visually enhancedin red/pink tonalities.
 2. The system of claim 1, wherein a pixel isdetermined to have a land background.